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has been cited by the following article:

• TITLE: Day-Ahead Probabilistic Load Flow Analysis Considering Wind Power Forecast Error Correlation

  AUTHORS: Qiang Ding, Chuancheng Zhang, Jingyang Zhou, Sai Dai, Dan Xu, Zhiqiang Luo, Chengwei Zhai

  KEYWORDS: Wind Power, Time Series Model, Forecast Error Distribution, Forecast Error Correlation, Probabilistic Load Flow, Gram-Charlier Expansion

  JOURNAL NAME: Energy and Power Engineering, Vol.9 No.4B, April 6, 2017

  ABSTRACT: Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.